> It when will the past become good enough for daily use?

We should all know by now that it depends on who you are. For conversational use, toolcalling and structured response, it was a few years ago. For coding it will probably be a few months from now. And for advanced 3D/computer use/biology/engineering applications, we're probably looking at a longer time horizon.

We have GPUs and NPUs capable of accelerating models this size on-device, most of the time. Replacing that flexible logic block with a static ASIC is a downgrade, especially since users don't ever have 1000+ tok/s or batched request requirements. The on-device acceleration is perfectly fine for >8B model inference.

I get that people are starry-eyed, and that HN is supposed to be the place for curious inquiry. But creating a coprocessor that uses a redundant, static memory system that gives users no noticeable advantages is much more expensive and stupid than investing in a CUDA-style GPGPU architecture.

Any older models are better in many ways. Won't surprise me when we reach a point that new LLM models start collapsing in quality (while at the same time probably becoming better for the adtech industry), and older models are the premium.